1. Field of the Invention
This invention pertains to an improved data processing system capable of evolving relative awareness states to drive response functions. In particular, this is a generally and massively parallel processing methodology and system applicable to any type of data without knowledge bases or specific conditional processing rules.
2. Description of the Prior Art
A process called the Nearest Neighbor logic is known in the prior art. One could describe this process as a "top down" manner where conditional human logic (the "top") and specifically proscribed algorithms with limited, yet specific application (also the "top"), are employed against those factors deemed to qualify as nearest neighbors (the "down" or raw data portion) according to some means for assessing proximity, usually physical distance.
However, the prior Nearest Neighbor art does not take a comprehensive approach that would then be universally applicable to all kinds of data types and processing objectives. That is, the prior art would take, for instance, a given processing cell, such as a visual pixel cell from anywhere in some visual sensing grid, and would identify the surrounding pixel cells identifying which are closer or further to a cell in question. All such cells in these systems would then identify their nearest neighbors as well and compile them or be able to determine them as needed to guide processing. It should be noted that the prior art treats each focus cell as equally relevant as any other focus cell to start. They all have neighbors and the nearest neighbor logic is not universally applied as a center of gravity, in a logical sense. That is, any cell could be a focus cell in varying situations. In the present invention, the same applies only in a relative sense in first figuring out which cells surround each other. But then the present invention goes further in assigning but one ultimate focus cell in terms of always having the highest default relevance during processing. Each cell in the present invention has a default relevance due to its position.
The present invention looks to the force of gravity, as later described, to derive a new process to configuration and processing methodologies. While aspects of nearest neighbor methodology are involved, the present invention provides new methods and applications. Each cell in a visual embodiment of the present invention, for instance, would not treat each visual cell as equal to any other. Instead, the current invention holds to a new comprehensive notion of what Nearest Neighbor logic actually represents in terms of a far broader and universally applicable logic that applies in a bottom up approach (where the data drives how the system responds) and is inherently, and massively parallel by nature. That is, the present invention does not just apply a proximity test to various cells or other factors. The present invention treats Nearest Neighbor logic as a subset of a new comprehensive logic that emulates the logic of the force of gravity. Thus, instead of a localized piece of top down logic that measures relative nearness, what is herein called "Gravity Logic" has a universal range independent of what anything in any data stream may mean in a higher sense of awareness. That is, like the force of gravity, the logic of gravity when implemented as prescribed herein, acts independently on the data forms that encounter the embedded default logic and configurations that are designed with Gravity Logic as their guide.
The present invention embodies this logic first in the way the invention and its resources are configured. That is, Gravity Logic offers guidance in how resources need to be configured. Then Gravity Logic provides guidance in the very processing logic that is embodied in that configuration as well as how memory resources function if configured. Gravity logic is implemented within the resources configured and functions within the level configured. That means, if no memory retention resources are configured (not to be confused with random access memory), then no gravity driven memory dynamic will be present either.
This establishes a system with automatic filtering capabilities, herein called gravitational relevance filters, that act by default (automatically without conditional databases or specifically applicable algorithms required) on any data forms flowing through it so that the data forms self organize into associations that represent the default mandates of the Gravity Logic that is present in such systems. For example, these associations are complex arrays of the original data pixels linking from whatever data types an embodiment is configured to process. The associations link the original data samples in terms of time, space, and form factors (the actual data values of that data type) that pass the tests of Gravity Logic at the various convergent nodes configured.
There are many Nearest Neighbor related inventions one could reference to gain a broader understanding of that logic and to then understand how and where the present invention differs, and where the present invention ultimately extends into entirely new kinds of functionalities and performance capabilities. In U.S. Pat. No. 5,305,393, "Exhaustive Hierarchical Near Neighbor Operations On An Image", Mahoney offers a parallel processing opportunity for analyzing images and only images. Mahoney states, "A near neighbor is a pixel that meets an appropriate criterion, such as the nearest black pixel in a binary image." Note how the specific reference to conditional logic such as searching for a specific color, unlike the present invention. Mahoney claims to be a more efficient "exhaustive near neighbor technique" and produces what he calls a "hierarchy of data items." His meaning of hierarchy is different from the present invention's notion of what will be discussed herein called the "Hierarchy of Awareness". In the present invention, every new convergent node encountered is another step up this "Hierarchy of Awareness". Each convergent node increases the size of the data items associated. That means, the larger the association of data forms in the present invention, the higher the relative awareness, generally speaking, and the further up the "Hierarchy of Awareness" that data form is. By default, in the present invention the larger the data form, the more highly evolved it is. Specific, relevant awareness akin to conditional or context based human awareness is an emergent property of the present invention pertaining to robust embodiments and is a relative awareness state. Awareness states in the present invention are specifically defined as any data association ranging from but one pixel of one data type to perhaps billions of multivariate pixels across many time and space cross sections of data encounters involving many response potentials, with human level awareness representing the highest known such states. Potential awareness is seen as a function of resources configured according to Gravity Logic.
Mahoney describes his hierarchy as, "For a given pixel the hierarchy indicates an approximate near neighbor." His hierarchy is thus a table or data list which could identify all the relative proximities each cell has to every other. But this is impractical as he notes, and instead he resorts to processing that relies on smaller regions of the visual image he calls zones. The present invention does not function in this way. The present invention focuses on the center of the visual time frame with standardized visual data samples with each pixel's relevance determined by where it is in relation to the center, if one were comparing a visual embodiment of the present invention. These discussion and comparative points in no way limit the ultimate scope of the present invention. That is, in the present invention the center of the visual grid is always, by default, the most important pixel site for any image processed, until some higher awareness state emerges to dictate otherwise, as will be explained later. These basic default states that are universally applicable to any data type truly distinguish the present invention from all others in that regard. For now, the present invention relies on basic universal defaults to get started but once started and assuming memory and learning resources are configured (they need not be, but that limits the potential awareness as, for example, a lack of memory in a human would limit their potential awareness), then experience begins to converge with new data streams and memories and feedback begins to influence what the defaults initially converged on as choices. The present invention assumes memories are relevant until proved otherwise. Memories are higher awareness states that override the basic defaults. Memories become the basic defaults that apply. No other nearest neighbor invention has such initial default states that are universally applicable to all data types which is one major advantage of the present invention over the prior art. Gravity Logic alone allows the present invention to converge on the memories that are most relevant so that the present invention can rely on those memories in deciding what to do. Then, Gravity Logic defaults focus on apparent change as will be described to isolate those aspects of memory that failed to meet default expectations. This allows the present invention to focus only on those aspects that differ and to iterate towards ever more perfected response states.
Parallel processing is a major aspect of the present invention. Many nearest neighbor inventions offer parallel processing opportunities but none like the present invention. Mahoney's parallel methodology differs because he has to deal with parallel communications between what he calls owner cells and near neighbors that results in "communication collisions". He resorts to collision resolution logic such as "by accepting only the first value to arrive, by allowing later values to overwrite previous values, or by combining the colliding values using a function such as the maximum or minimum." The present invention's parallel processing logic functions without concern for collisions or which data gets where first. Unlike all other parallel inventions, the present invention is parallel by nature, which is a major advantage because it makes possible real time resolution of enormously complex data streams. Also, unlike any other invention, each data item, from individual pixels to complex associations thereof way up the Hierarchy of Awareness, are all unique form in terms of time, space and form.
The present invention may seem like a neural network because it claims to be able to evolve relative awareness states and response states that allow for a learning dynamic. However, the closest analogy seems to be more like a domino strategy where you configure the invention as prescribed by Gravity Logic and the dominoes are the various data types that have been configured for processing. These trigger all sorts of defaults as they flow through the system and these defaults are self directed outcomes based on the original pattern of the dominoes themselves. When memory resources are configured, these provide the system with the ability to adapt the basic defaults around the memory resources so that these resources become the defaults that apply as the system experiences things.
In pattern recognition and other neural network like inventions the concept of Relevance has always been problematical as no one until now has had a base theory for defining what relevance is in any situation nor has the field found any common ground for even defining what relevance really is. Mahoney mentions the notion of what is relevant in a given image and this notion deviates greatly from the present invention's revolutionary approach which is characterized as relevance by default according to Gravity Logic, i.e., gravitational relevance, as will be explained more later. Mahoney states that he does not deal with all pixels and particularly those that are not relevant. He then backs into what is meant by relevant by stating that "an attention mechanism" is needed to conditionally determine what is relevant in any given image. This approach requires human context and some kind of existing reference point to specify relevance. That means, designers have to anticipate what may be encountered and set up relevance filters specified in advance, which of course cannot possibly deal with the contextual potentials in just one image let alone multivariate perspectives on reality as the present invention can.
In the present invention, relevance is universal, independent of context to start (which is how the default processing functions) and always consistently converged upon. Relevance is defined herein as a function of Gravity Logic where, for now, things that are found to be relatively most central (not just in a visual time frame, but in any type of data's time frame) are by default most relevant, and only those data forms that survive competitive convergent node processing in the present invention are deemed relevant by default (where applied Gravity Logic in program and processing logic automatically sorts out what forms are relatively more fit than others). Relevance and awareness are relative states in the present invention that are a function of experience and current encounters. There are no conditional or specifically applicable algorithms in the present invention. This approach works on all data types. The only algorithm of any kind in the present invention is Gravity Logic, but it is not conditional or specific to certain situations as are all other algorithms. Gravity Logic is a universal default logic only roughly related to nearest neighbor logic but independent of the actual data. Once implemented, data filters through the convergent node process and emerges as relatively gravitationally relevant data patterns.
As will be detailed, the present invention relies on what one might characterize as nearest neighbor defaults that apply first to time factors, then spatial address factors and then data form factors independent of what values are contained in any data stream being processed. So one contrasting point are these standardized time, space, and form factors that are not required in any prior art involving nearest neighbor logic but which are required in the present invention (although implicit relative time and space factors based on sequence or relative position rather than explicit time stamps and space address factors may be utilized to prioritize data forms and define their relative relevance to each other).
Gravity Logic specifically operates on proximity in time. We first determine which data items pertain to a given time frame. Each data type comes in separate time frames at some specified sampling rate.
Then Gravity Logic applies to the relative cross sections of space. Spatial addressing factors (binary addresses typically) are utilized at whatever spatial resolution an embodiment has specified. Spatial addresses provide the reference by which to converge on which data forms within a given time frame are proximate to each other. This is where the similarity to nearest neighbor methodology begins and ends. Such neighbors are spatially relative to the center of the time frame. (Think of time frames in terms of visual pictures. Unlike pictures, many kinds of data will not line up in grid like manner. That's where Gravity Logic extended to data forms then provides a way to emulate the logic of mass to establish relative center of gravity based on relative size).
So lastly the new Gravity Logic then compares the data forms' values within a time frame to each other and ends up converging on those sites which have the closest fitting data values. Like values that are neighbors attract.
As such, one important advantage of the present invention is that an embodiment of the present invention is not concerned with any specific pixel to pixel values per se as all other nearest neighbor inventions are. Here, just any pixel to pixel results that have high degrees of fit (such as two matching neighboring red pixels or blue pixels or any color, or if we were dealing with stock market data, two 100 share trades of some common stock at $110.50 per share versus two 100 share trades where one trade was at $110.50 per share and another trade was at $109.75 per share within the same time frame. If the number of shares differ, then the total value of the trade would differ as well). The present invention defaults into assuming these like valued samples within a time frame that are spatially proximate are by default related to each other. This does not mean a red pixel in the middle is assumed related to one that is not surrounding it as with some other nearest neighbor inventions. Instead, Gravity Logic requires some resolution be specified as in horizontal and vertical neighbors or horizontal, vertical and diagonal neighbors, or actual physical coordinates and distances discernible due to such higher resolution coordinates. This is one of the ways Gravity Logic is imbedded into the configuration before processing actually starts. Once gravity resolution is specified, spatial resolution is effectively specified for that data type as well because gravity is treated as a function of space itself in the present invention. Thereafter, the set of nearest neighbors relevant to each cell is fixed and always pertains regardless of what image is processed. But note, that even though each cell in a visual grid has a fixed set of nearest neighbors as other nearest neighbor inventions, the present invention distinguishes among these in terms of proximity to the center of the time frame. This means each cell's relevance in terms of every other cell is always consistently determined regardless of what is processed. While many inventions may have processes that focus on the center of things, none apply to data forms with time, space and form values and none process their data forms as herein prescribed.
As mentioned, in the present invention patterns emerge that are first based on overall time, then space and then form proximity. Each data type processes in terms of its own time frames so that patterns within each time frame first emerge for each data type. Nothing like that exists in other inventions. This offers parallel processing advantages because each different data type resolves concurrently and does so in relation to cross sections of time and space and form.
Then, the present invention continues to converge on still higher awareness patterns based on apparent change across time frames for each data type individually. We now have the advantage of concurrently converging on relative change in terms of each data type configured. The ability to automatically discern relative change is a function of applied Gravity Logic. This is so because once Gravity Logic converges on specific patterns within time frames (herein called potential objects of a single data type), that same logic then allows an embodiment to rely on what it experienced in the initial time frame. As such, and by default, any differences in space and form stand out and they do so in an inherently parallel manner making for a great processing advantage. In the present invention, change is thus a function of Gravity Logic as well.
Once change is discerned in an embodiment with the requisite resources for each data type, then even more complex patterns (awareness states are defined herein as any data pixel or higher association thereof in the present invention uniquely defined in terms of time, space and form factors) are discerned across data types when the present invention relies on time and space factors to compare disparate data types.
U.S. Pat. No. 5,140,670 Cellular Neural Network (CNN) issued to Leon Chua, Nearest Neighbor logic is relied upon for building the neural network. His invention relies on the physical proximity of neighboring circuits and the speed with which it takes signals to get from any circuit site to any other to guide the system in deciding what data to process. His invention is essentially a processing device that can be employed by any neural network. His invention relies completely on the neural network application to conditionally dictate how the CNN will be utilized. He mentions templates and dynamic rules. Once the neural application is modified to the requirements of the CNN it then relies on the CNN's tendency to settle into equilibrium states based on the proximity of cells (circuits) to other cells and the time it takes for information to flow back and forth within such a system.
Of critical distinction and as with Mahoney, the CNN invention does not make a distinction between the very center of a visual grid, for instance, or the center of any other data type time frame. Instead, the CNN again takes each cell and sees layers of cells around each cell (in two or three dimensions) as if all the cells were equally relevant to start. The present invention specifically starts with the central region of each data time frame as most relevant by default. In the present invention, relevance is a function of the center just as gravity as a real force (rather than a logic) is a function of proximity to the center (gravity's attraction decreases with the inverse square of the distance from the center of a given body, but the general notion of decreasing importance with distance in general is applied herein, meaning distance in time, space, or form or collectively).
Further, the CNN invention makes no mention of standardized data forms with time, space, and form factors. The present invention specifically processes each data type configured for processing independent of every other data type and it does so by processing in terms of time frames and spatial proximities within each time frame with relevance emanating from the center of the time frame or from what one would call the center of gravity when data form values are processed (the larger the association, the larger the assumed data mass, the more relevant by default).
For example, other inventions designed for image processing might be able to apply conditional logic and specialized algorithms or rules to discern object outlines, or certain kinds of shading, and so on in a given image. These findings might be anywhere in the image and no priority is necessarily assigned unless the designer specifically defines what will gain a processing priority. The present invention works in a completely different manner.
First, the above approach where specific things are looked for in an image is an example of what one might call an existing higher awareness state also known as a top down search. One could employ huge manmade databases with templates of form that could be searched and matched sequentially or in parallel to a new image stream to perhaps find something that is similar to what is in the database. The designer's goals are imposed on the CNN or other nearest neighbor inventions to control and dictate how those types of inventions will function. While such impositions can be applied to the present invention, they need not be. The universal base approach operates without such impositions to evolve relative pattern awareness levels automatically and independent of such conditional logic from the pixel level upward. The present invention assumes for initial encounters, by default, that whatever it converges upon in the center of the data time frame is most relevant. As such, a base awareness is attained automatically by an embodiment on its own from the start. From this base level, vastly more complex, contextual awareness states can ultimately emerge as one applies the present invention's methodology to greater and greater amounts of resources configured as prescribed. Context is a function of memory resources. The more resources the more resolved memory can be in relation to current encounters. That is, relative awareness and response potentials configured to be driven by such states in the present invention are essentially a function of the resources and response states configured and the number of different data types and resolutions configured. Just as image resolution is a function of how many and how refined the pixels are, memories are like contextual pixels in the present invention.
Some prior art related to this invention's bottom up approach would be cellular automata which are pieces of logic with certain prescribed response states which automatically cause the cellular automata in a given logical environment to evolve on their own with various emergent outcomes that seem to mimic basic biological processes. The present invention does not assign specific response states to each cell. The present invention relies on gravitational default logic with generic response states to drive response potentials initially. Then those initial default responses, which would be at the center of the response range, serve as baseline responses against which a new kind of learning dynamic can begin to iterate towards ever more perfected responses in relation to data streams that are encountered and captured in memory. This learning dynamic is again based on the present invention's ability to discern relative differences between one time frame and another and the potential objects therein. Thus any change or differences discerned between a memory and a new data stream, however complex, emerges automatically and the invention focuses resources on those most proximate aspects of change so discerned. This capability is unique to this invention.
One can look at the CNN invention and see in its FIG. 3 and FIG. 4 diagrams that show how circuits can be configured in relation to each other. The present invention would take the CNN approach and specify that the black cell in the center of its FIG. 4 is not just the way each cell in the grid views the circuits around it, but that the cell/circuit in the center is by default most relevant. This is accomplished by using an addressing logic that imbeds the ability to discern relative distance from the center. His FIG. 4 shows one cell/circuit's view of the circuits around it. Each of his cells shares this view but none of them holds the default priority over all others. Thus, the present invention would migrate the CNN invention to an entirely new processing opportunity by specifying addresses that captured the logic of gravity where the very center cell's data samples always stand out as the most relevant site and samples regardless of what the system actually processes. This is a great distinction and only begins to illustrate the significant differences between the present invention and those mentioned and all other nearest neighbor inventions as well as any neural network or system of that ilk.
The present invention is universally applicable to all data types and all data processing objectives. Many nearest neighbor inventions apply only to visual processing inventions or to finding a route from a map database, or to specific neural network applications. The present invention applies to all data types as long as those forms adhere to the requirements that they follow standardized time, space, and form requirements. No other invention requires time and space factors be associated with all data types from the moment an embodiment encounters a data stream all the way through the convergent node processing steps where new data associations emerge, where each emerges with their own time and space factors along with composite form factors from the converging forms.
The present invention holds that solutions to any kind of problem are really nothing more than relative awareness states, where a certain pattern that is either the very best (exhaustive) solution, or a relatively optimal one (where relative can mean very nearly the best or quite crude), are converged upon. Once a solution (a result, a pattern) is arrived upon, that pattern can then be configured to interface with the system so that various response states can be influenced by such solutions. In the present invention, these solutions or patterns would trigger default responses based upon Gravity Logic once again, where a system, devoid of memories and experiences from which to draw upon initially, would nevertheless have a default response that would trigger within the center of the response range because Gravity Logic defaults again pertain to the center of a range and in the case of responses is the center of the response range. Such initial reactions then provide the basis for memories relating response potentials and the range of response potentials with newly encountered patterns and to then have open a new kind of learning dynamic. The learning dynamic of the present invention is unlike any other. It relies on Gravity Logic yet again and does so in the following regard. It assumes that all solutions or patterns are only relevant if they survive change. As such, all discerned patterns that survive within the present invention's competitive parallel environment are assumed by default to be relevant and that all aspects of those patterns pertain. Thus, any differences that are discerned (by Gravity Logic defaults), as detailed later, stand out and obtain a processing priority which means the embodiment devotes resources to first focus on discerned change. Further, due to Gravity Logic, embodiments will, by default, focus on patterns (data associations) that are most proximate to the embodiment itself because the embodiment is the most central location. And most importantly, an embodiment configured to deal with time series data encounters will automatically focus on change and change that involves itself. This leads to the emergence of what is herein characterized as a relative sense of self in relation to experience and whatever the embodiment's resources allow it to attain in terms of relative awareness states. The sense of self awareness emerges in the present invention because all learning focuses resources on change that involves self first The learning dynamic thus emanates outward in the same logical manner that the force of gravity does, from the center outward.
Nearest neighbor inventions exist that pertain to non visual kinds of data streams. In particular is U.S. Pat. No. 5,272,638 Systems and Methods for Planning the Scheduling Travel Routes issued to Cynthia Martin et al. The one key distinction between this invention and the present one is "An array of randomly ordered sequences is created with each sequence representing a unique ordering of the destinations to be visited." The key word being randomly ordered. The present invention acts consistently according to the logic of gravity. There are no random processes in the present invention unless such processes are desired to influence the basic gravitational defaults or for other possible reasons. That is, the present invention works on gravity defaults alone, however it has the flexibility to be influenced by any outside logic or random processes if designers wish to employ such means. This is possible because all such outside factors are treated as if they were existing memory states or as if they were part of the configuration (capable of changing or fixed), such as a resource constraint or a range or resolution constraint. This will be explained later, but it illustrates another advantage of the present invention in its enormous flexibility.
U.S. Pat. No. 5,272,638 also relies on genetic cellular automation to determine near optimal sequence of destinations. The methodology in U.S. Pat. No. 5,272,638 is limited to travel route applications and does not indicate how the method can be extended to accomplish generalized pattern recognition by data type in terms of time frames, spatial proximity within time frames or proximity amongst data forms within those time frames as the present invention teaches. The travel invention makes no mention of time frames as the base organizing theme of the data forms being processed. It does rely on spatial proximity as do all nearest neighbor inventions, but again there is no ultimate center of logic. Instead that invention relies on random generation of possible paths and then relies on sorting those according to proximity or other nearness criteria (such as the user choosing only highways versus wanting just any possible route).
All of the system factors indicated in the various nearest neighbor inventions could pertain to the present invention with the requirement they be reconfigured according to Gravity Logic. In applying the present invention to a traveling salesman type embodiment, the various data forms sampled would be similar to those in U.S. Pat. No. 5,272,638 with the addition of time stamps (in explicitly rendered embodiments) and spatial addresses that would be similar in form to what is in U.S. Pat. No. 5,272,638 (where actual latitude and longitude values are used to ascertain distance). The present invention would not begin processing with random associations, but would instead iterate from each possible site that is proximate to the starting point and destination point and do so in parallel. That is, if there are 200 possible sites defined that are proximate to the start and destination in the database (the present invention would treat these database values as samples), then there are some n.times.(n-1) or 200.times.199 unique associations among those points to begin with. The present invention would leverage this fact and apply Gravity Logic to discern which of these associations are most proximate in space and form within the time frame. If the application were enormously data intensive where instead of 200 perhaps 2 billion possible sites existed in some huge network where near optimal paths were sought quickly, then the present invention would automatically ration its resources to focus on the most gravitationally relevant sites first, i.e., those that are most proximate as well as the largest sites if data form values are of any importance. That is, if we had some number of salespeople and we wanted them to travel to the largest 40 US cities because population is a proxy for sales potential, then population size (the value of New York City as a pixel point) would become a gravitationally more significant site then Houston up to a point. That point would be the relative distance. That is, if you started in Dallas, then Houston is much closer than New York and might overcome the much larger population size just as the moon is almost as gravitationally significant to tides on earth as the sun is even though the sun is a million times larger than the moon, it is 93 million miles away versus 240,000 miles. The same kind of resolution potentials pertain in the present invention in regards to data values. Note, in the above commentary we are imposing external factors on the otherwise gravity default driven process because the embodiment cannot know what we want unless we specify our objective. Thus, the flexibility of the present invention to allow us to interface external factors into the default awareness process offers enormous advantages for situations where people can interface with an embodiment to explore alternative outcomes the human partner is in a position to evaluate. In these situations the embodiment is a pattern generation unit .The awareness is limited in that regard. We've just discussed an embodiment without a learning dynamic.
But again, no random logic is applied in the present invention, but it could be to provide ways for modest embodiments to be made interactive with users who wish to control and directly influence the pattern generation potentials, but that fact in no way makes the present invention remotely akin to these others.
U.S. Pat. No. 5,272,638 also says "to avoid redundant storage of latitude and longitude pairs, a list of unique intersection data structures is created." A similar outcome occurs in the present invention but it occurs automatically and consistently within the present invention and occurs in any data type by time frame according to spatial proximity of each data type and form proximity. Thus, U.S. Pat. No. 5,272,638's unique list is a human effort at starting out with a non redundant data list, not a dynamic process that operates on disparate data types. The reader must note the distinction between a table of humanly compiled data options and one dynamically evolved and an invention that is limited to one very conditionalized travel application versus the present invention which is universally applicable to all data types without conditional human logic without the need for random process logic.
U.S. Pat. No. 5,272,638 also states, "A node may be evaluated multiple times because there may be more than one unique path that leads to it, but only the node values with the lowest time remains on the final path list." In the present invention, the objective of a traveling salesman embodiment would of course seek the same result, albeit accomplished in quite a different manner. Again, if one eliminated the random operation specified in U.S. Pat. No. 5,272,638, the invention does not work. The present invention does work and that fact alone completely distinguishes the two inventions. U.S. Pat. No. 5,272,638 would have to apply exhaustive comparisons or settle for some number of alternatives obtained in order they appear in the database and sort those out and settle for what that approach might provide as a near optimal solution within these clearly less than optimal choices. The present invention's gravitational approach always converges upon the most optimal "pixel pairs" within resource constraints thereby assuring at least a relative level of near optimal choices regardless of how constrained the embodiment may be. This fact distinguishes the present invention not only from U.S. Pat. No. 5,272,638 but all others. That is, the present invention's approach always converges on the relatively best alternatives within the resource or other processing constraints (such as time) that may pertain when any situation is contemplated. Even in the extreme case where only 1 choice may be possible, the present invention's approach still defaults to a choice that adheres to Gravity Logic and is therefore better on average than a random choice which could of course happen to be the very best choice possible, but which will average out at a middle mediocrity over many applications.
As will be described in greater detail hereinafter, the method and system of the present invention differs from those previously proposed and employs a number of novel features that render it highly advantageous over the prior art.